OpenRaiser/Intern-Atlas huggingface dataset:
Reinforcement Learning-Based Joint Self-Optimisation Method for the Fuzzy Logic Handover Algorithm in 5G HetNets
Venue: arXiv cs.NI 2021
Year: 2021

5G heterogeneous networks (HetNets) can provide higher network coverage and system capacity to the user by deploying massive small base stations (BSs) within the 4G macro system. However, the large-scale deployment of small BSs significantly increases the complexity and workload of network maintenance and optimisation. The current handover (HO) triggering mechanism A3 event was designed only for mobility management in the macro system. Directly implementing A3 in 5G-HetNets may degrade the user mobility robustness. Motivated by the concept of self-organisation networks (SON), this study developed a self-optimised triggering mechanism to enable automated network maintenance and enhance user mobility robustness in 5G-HetNets. The proposed method integrates the advantages of subtractive clustering and Q-learning frameworks into the conventional fuzzy logic-based HO algorithm (FLHA). Subtractive clustering is first adopted to generate a membership function (MF) for the FLHA to enable FLHA with the self-configuration feature. Subsequently, Q-learning is utilised to learn the optimal HO policy from the environment as fuzzy rules that empower the FLHA with a self-optimisation function. The FLHA with SON functionality also overcomes the limitations of the conventional FLHA that must rely heavily on professional experience to design. The simulation results show that the proposed self-optimised FLHA can effectively generate MF and fuzzy rules for the FLHA. By comparing with conventional triggering mechanisms, the proposed approach can decrease the HO, ping-pong HO, and HO failure ratios by approximately 91%, 49%, and 97.5% while improving network throughput and latency by 8% and 35%, respectively.
Assessing Practitioner Beliefs about Software Engineering
Venue: arXiv cs.SE 2021
Year: 2021

Software engineering is a highly dynamic discipline. Hence, as times change, so too might our beliefs about core processes in this field. This paper checks some five beliefs that originated in the past decades that comment on the relationships between (i) developer productivity; (ii) software quality and (iii) years of developer experience. Using data collected from 1,356 developers in the period 1995 to 2006, we found support for only one of the five beliefs titled "Quality entails productivity". We found no clear support for four other beliefs based on programming languages and software developers. However, from the sporadic evidence of the four other beliefs we learned that a narrow scope could delude practitioners in misinterpreting certain effects to hold in their day to day work. Lastly, through an aggregated view of assessing the five beliefs, we find programming languages act as a confounding factor for developer productivity and software quality. Thus the overall message of this work is that it is both important and possible to revisit old beliefs in SE. Researchers and practitioners should routinely retest old beliefs.
Optimal-Time Queries on BWT-runs Compressed Indexes
Venue: arXiv cs.DS 2021
Year: 2021

Indexing highly repetitive strings (i.e., strings with many repetitions) for fast queries has become a central research topic in string processing, because it has a wide variety of applications in bioinformatics and natural language processing. Although a substantial number of indexes for highly repetitive strings have been proposed thus far, developing compressed indexes that support various queries remains a challenge. The run-length Burrows-Wheeler transform (RLBWT) is a lossless data compression by a reversible permutation of an input string and run-length encoding, and it has received interest for indexing highly repetitive strings. LF and $ϕ^{-1}$ are two key functions for building indexes on RLBWT, and the best previous result computes LF and $ϕ^{-1}$ in $O(\log \log n)$ time with $O(r)$ words of space for the string length $n$ and the number $r$ of runs in RLBWT. In this paper, we improve LF and $ϕ^{-1}$ so that they can be computed in a constant time with $O(r)$ words of space. Subsequently, we present OptBWTR (optimal-time queries on BWT-runs compressed indexes), the first string index that supports various queries including locate, count, extract queries in optimal time and $O(r)$ words of space.
A Complete Axiomatisation for Quantifier-Free Separation Logic
Venue: arXiv cs.LO 2021
Year: 2021

We present the first complete axiomatisation for quantifier-free separation logic. The logic is equipped with the standard concrete heaplet semantics and the proof system has no external feature such as nominals/labels. It is not possible to rely completely on proof systems for Boolean BI as the concrete semantics needs to be taken into account. Therefore, we present the first internal Hilbert-style axiomatisation for quantifier-free separation logic. The calculus is divided in three parts: the axiomatisation of core formulae where Boolean combinations of core formulae capture the expressivity of the whole logic, axioms and inference rules to simulate a bottom-up elimination of separating connectives, and finally structural axioms and inference rules from propositional calculus and Boolean BI with the magic wand.
Global Data Science Project for COVID-19
Venue: arXiv cs.SI 2021
Year: 2021

This paper aims at providing the summary of the Global Data Science Project (GDSC) for COVID-19. as on May 31 2020. COVID-19 has largely impacted on our societies through both direct and indirect effects transmitted by the policy measures to counter the spread of viruses. We quantitatively analysed the multifaceted impacts of the COVID-19 pandemic on our societies including people's mobility, health, and social behaviour changes. People's mobility has changed significantly due to the implementation of travel restriction and quarantine measurements. Indeed, the physical distance has widened at international (cross-border), national and regional level. At international level, due to the travel restrictions, the number of international flights has plunged overall at around 88 percent during March. In particular, the number of flights connecting Europe dropped drastically in mid of March after the United States announced travel restrictions to Europe and the EU and participating countries agreed to close borders, at 84 percent decline compared to March 10th. Similarly, we examined the impacts of quarantine measures in the major city: Tokyo (Japan), New York City (the United States), and Barcelona (Spain). Within all three cities, we found the significant decline in traffic volume. We also identified the increased concern for mental health through the analysis of posts on social networking services such as Twitter and Instagram. Notably, in the beginning of April 2020, the number of post with #depression on Instagram doubled, which might reflect the rise in mental health awareness among Instagram users. Besides, we identified the changes in a wide range of people's social behaviors, as well as economic impacts through the analysis of Instagram data and primary survey data.
Estimating semantic structure for the VQA answer space
Venue: arXiv cs.CV 2021
Year: 2021

Since its appearance, Visual Question Answering (VQA, i.e. answering a question posed over an image), has always been treated as a classification problem over a set of predefined answers. Despite its convenience, this classification approach poorly reflects the semantics of the problem limiting the answering to a choice between independent proposals, without taking into account the similarity between them (e.g. equally penalizing for answering cat or German shepherd instead of dog). We address this issue by proposing (1) two measures of proximity between VQA classes, and (2) a corresponding loss which takes into account the estimated proximity. This significantly improves the generalization of VQA models by reducing their language bias. In particular, we show that our approach is completely model-agnostic since it allows consistent improvements with three different VQA models. Finally, by combining our method with a language bias reduction approach, we report SOTA-level performance on the challenging VQAv2-CP dataset.
Object Detection in the DCT Domain: is Luminance the Solution?
Venue: arXiv cs.CV 2021
Year: 2021

Object detection in images has reached unprecedented performances. The state-of-the-art methods rely on deep architectures that extract salient features and predict bounding boxes enclosing the objects of interest. These methods essentially run on RGB images. However, the RGB images are often compressed by the acquisition devices for storage purpose and transfer efficiency. Hence, their decompression is required for object detectors. To gain in efficiency, this paper proposes to take advantage of the compressed representation of images to carry out object detection usable in constrained resources conditions. Specifically, we focus on JPEG images and propose a thorough analysis of detection architectures newly designed in regard of the peculiarities of the JPEG norm. This leads to a $\times 1.7$ speed up in comparison with a standard RGB-based architecture, while only reducing the detection performance by 5.5%. Additionally, our empirical findings demonstrate that only part of the compressed JPEG information, namely the luminance component, may be required to match detection accuracy of the full input methods.
CAN-D: A Modular Four-Step Pipeline for Comprehensively Decoding Controller Area Network Data
Venue: arXiv cs.OH 2021
Year: 2021

CANs are a broadcast protocol for real-time communication of critical vehicle subsystems. Original equipment manufacturers of passenger vehicles hold secret their mappings of CAN data to vehicle signals, and these definitions vary according to make, model, and year. Without these mappings, the wealth of real-time vehicle information hidden in the CAN packets is uninterpretable, impeding vehicle-related research. Guided by the 4-part CAN signal definition, we present CAN-D (CAN-Decoder), a modular, 4-step pipeline for identifying each signal's boundaries (start bit, length), endianness (byte order), signedness (bit-to-integer encoding), and by leveraging diagnostic standards, augmenting a subset of the extracted signals with physical interpretation. We provide a comprehensive review of the CAN signal reverse engineering research. Previous methods ignore endianness and signedness, rendering them incapable of decoding many standard CAN signal definitions. Incorporating endianness grows the search space from 128 to 4.72E21 signal tokenizations and introduces a web of changing dependencies. We formulate, formally analyze, and provide an efficient solution to an optimization problem, allowing identification of the optimal set of signal boundaries and byte orderings. We provide two novel, state-of-the-art signal boundary classifiers-both superior to previous approaches in precision and recall in three different test scenarios-and the first signedness classification algorithm which exhibits a $>$97\% F-score. CAN-D is the only solution with the potential to extract any CAN signal. In evaluation on 10 vehicles, CAN-D's average $\ell^1$ error is 5x better than all previous methods and exhibits lower ave. error, even when considering only signals that meet prior methods' assumptions. CAN-D is implemented in lightweight hardware, allowing for an OBD-II plugin for real-time in-vehicle CAN decoding.
Towards Integrating Fairness Transparently in Industrial Applications
Venue: arXiv cs.CY 2021
Year: 2021

Numerous Machine Learning (ML) bias-related failures in recent years have led to scrutiny of how companies incorporate aspects of transparency and accountability in their ML lifecycles. Companies have a responsibility to monitor ML processes for bias and mitigate any bias detected, ensure business product integrity, preserve customer loyalty, and protect brand image. Challenges specific to industry ML projects can be broadly categorized into principled documentation, human oversight, and need for mechanisms that enable information reuse and improve cost efficiency. We highlight specific roadblocks and propose conceptual solutions on a per-category basis for ML practitioners and organizational subject matter experts. Our systematic approach tackles these challenges by integrating mechanized and human-in-the-loop components in bias detection, mitigation, and documentation of projects at various stages of the ML lifecycle. To motivate the implementation of our system -- SIFT (System to Integrate Fairness Transparently) -- we present its structural primitives with an example real-world use case on how it can be used to identify potential biases and determine appropriate mitigation strategies in a participatory manner.
Uplink and Downlink MIMO-NOMA with Simultaneous Triangularization
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we consider the uplink and downlink precoder design for two-user power-domain multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) systems. We propose novel uplink and downlink precoding and decoding schemes that lower the decoding complexity at the receiver by decomposing the MIMO-NOMA channels of the users into multiple single-input single-output (SISO)-NOMA channels via simultaneous triangularization (ST) of the MIMO channels of the users and a low-complexity self-interference cancellation at the receiver. The proposed ST MIMO-NOMA schemes avoid channel inversion at transmitter and receiver and take advantage of the null spaces of the MIMO channels of the users, which is beneficial for the ergodic achievable rate performance. We characterize the maximum ergodic achievable rate regions of the proposed uplink and downlink ST MIMO-NOMA schemes, and compare them with respective upper bounds, baseline MIMO-NOMA precoding schemes, and orthogonal multiple access (OMA). Our results illustrate that the proposed schemes significantly outperform the considered baseline MIMO-NOMA precoding schemes and OMA, and have a small gap to the respective upper bounds for most channel conditions and user rates. Moreover, we show that a hybrid scheme, which performs time sharing between the proposed uplink and downlink ST MIMO-NOMA and single-user MIMO, can improve performance even further.
The Backbone Method for Ultra-High Dimensional Sparse Machine Learning
Venue: arXiv cs.LG 2021
Year: 2021

We present the backbone method, a generic framework that enables sparse and interpretable supervised machine learning methods to scale to ultra-high dimensional problems. We solve sparse regression problems with $10^7$ features in minutes and $10^8$ features in hours, as well as decision tree problems with $10^5$ features in minutes.The proposed method operates in two phases: we first determine the backbone set, consisting of potentially relevant features, by solving a number of tractable subproblems; then, we solve a reduced problem, considering only the backbone features. For the sparse regression problem, our theoretical analysis shows that, under certain assumptions and with high probability, the backbone set consists of the truly relevant features. Numerical experiments on both synthetic and real-world datasets demonstrate that our method outperforms or competes with state-of-the-art methods in ultra-high dimensional problems, and competes with optimal solutions in problems where exact methods scale, both in terms of recovering the truly relevant features and in its out-of-sample predictive performance.
GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs
Venue: arXiv cs.DC 2021
Year: 2021

As the emerging trend of graph-based deep learning, Graph Neural Networks (GNNs) excel for their capability to generate high-quality node feature vectors (embeddings). However, the existing one-size-fits-all GNN implementations are insufficient to catch up with the evolving GNN architectures, the ever-increasing graph sizes, and the diverse node embedding dimensionalities. To this end, we propose \textbf{GNNAdvisor}, an adaptive and efficient runtime system to accelerate various GNN workloads on GPU platforms. First, GNNAdvisor explores and identifies several performance-relevant features from both the GNN model and the input graph, and uses them as a new driving force for GNN acceleration. Second, GNNAdvisor implements a novel and highly-efficient 2D workload management, tailored for GNN computation to improve GPU utilization and performance under different application settings. Third, GNNAdvisor capitalizes on the GPU memory hierarchy for acceleration by gracefully coordinating the execution of GNNs according to the characteristics of the GPU memory structure and GNN workloads. Furthermore, to enable automatic runtime optimization, GNNAdvisor incorporates a lightweight analytical model for an effective design parameter search. Extensive experiments show that GNNAdvisor outperforms the state-of-the-art GNN computing frameworks, such as Deep Graph Library ($3.02\times$ faster on average) and NeuGraph (up to $4.10\times$ faster), on mainstream GNN architectures across various datasets.
Backdoor Smoothing: Demystifying Backdoor Attacks on Deep Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

Backdoor attacks mislead machine-learning models to output an attacker-specified class when presented a specific trigger at test time. These attacks require poisoning the training data to compromise the learning algorithm, e.g., by injecting poisoning samples containing the trigger into the training set, along with the desired class label. Despite the increasing number of studies on backdoor attacks and defenses, the underlying factors affecting the success of backdoor attacks, along with their impact on the learning algorithm, are not yet well understood. In this work, we aim to shed light on this issue by unveiling that backdoor attacks induce a smoother decision function around the triggered samples -- a phenomenon which we refer to as \textit{backdoor smoothing}. To quantify backdoor smoothing, we define a measure that evaluates the uncertainty associated to the predictions of a classifier around the input samples. Our experiments show that smoothness increases when the trigger is added to the input samples, and that this phenomenon is more pronounced for more successful attacks. We also provide preliminary evidence that backdoor triggers are not the only smoothing-inducing patterns, but that also other artificial patterns can be detected by our approach, paving the way towards understanding the limitations of current defenses and designing novel ones.
RISCuer: A Reliable Multi-UAV Search and Rescue Testbed
Venue: arXiv cs.RO 2021
Year: 2021

We present the Robotics Intelligent Systems & Control (RISC) Lab multiagent testbed for reliable search and rescue and aerial transport in outdoor environments. The system consists of a team of three multirotor unmanned aerial vehicles (UAVs), which are capable of autonomously searching, picking up, and transporting randomly distributed objects in an outdoor field. The method involves vision based object detection and localization, passive aerial grasping with our novel design, GPS based UAV navigation, and safe release of the objects at the drop zone. Our cooperative strategy ensures safe spatial separation between UAVs at all times and we prevent any conflicts at the drop zone using communication enabled consensus. All computation is performed onboard each UAV. We describe the complete software and hardware architecture for the system and demonstrate its reliable performance using comprehensive outdoor experiments, and by comparing our results with some recent, similar works.
Multi Layer Neural Networks as Replacement for Pooling Operations
Venue: arXiv cs.CV 2021
Year: 2021

Pooling operations, which can be calculated at low cost and serve as a linear or nonlinear transfer function for data reduction, are found in almost every modern neural network. Countless modern approaches have already tackled replacing the common maximum value selection and mean value operations, not to mention providing a function that allows different functions to be selected through changing parameters. Additional neural networks are used to estimate the parameters of these pooling functions.Consequently, pooling layers may require supplementary parameters to increase the complexity of the whole model. In this work, we show that one perceptron can already be used effectively as a pooling operation without increasing the complexity of the model. This kind of pooling allows for the integration of multi-layer neural networks directly into a model as a pooling operation by restructuring the data and, as a result, learnin complex pooling operations. We compare our approach to tensor convolution with strides as a pooling operation and show that our approach is both effective and reduces complexity. The restructuring of the data in combination with multiple perceptrons allows for our approach to be used for upscaling, which can then be utilized for transposed convolutions in semantic segmentation.
Understanding and Resolving Performance Degradation in Graph Convolutional Networks
Venue: arXiv cs.LG 2021
Year: 2021

A Graph Convolutional Network (GCN) stacks several layers and in each layer performs a PROPagation operation (PROP) and a TRANsformation operation (TRAN) for learning node representations over graph-structured data. Though powerful, GCNs tend to suffer performance drop when the model gets deep. Previous works focus on PROPs to study and mitigate this issue, but the role of TRANs is barely investigated. In this work, we study performance degradation of GCNs by experimentally examining how stacking only TRANs or PROPs works. We find that TRANs contribute significantly, or even more than PROPs, to declining performance, and moreover that they tend to amplify node-wise feature variance in GCNs, causing variance inflammation that we identify as a key factor for causing performance drop. Motivated by such observations, we propose a variance-controlling technique termed Node Normalization (NodeNorm), which scales each node's features using its own standard deviation. Experimental results validate the effectiveness of NodeNorm on addressing performance degradation of GCNs. Specifically, it enables deep GCNs to outperform shallow ones in cases where deep models are needed, and to achieve comparable results with shallow ones on 6 benchmark datasets. NodeNorm is a generic plug-in and can well generalize to other GNN architectures. Code is publicly available at https://github.com/miafei/NodeNorm.
Knowledge Embedding Based Graph Convolutional Network
Venue: arXiv cs.LG 2021
Year: 2021

Recently, a considerable literature has grown up around the theme of Graph Convolutional Network (GCN). How to effectively leverage the rich structural information in complex graphs, such as knowledge graphs with heterogeneous types of entities and relations, is a primary open challenge in the field. Most GCN methods are either restricted to graphs with a homogeneous type of edges (e.g., citation links only), or focusing on representation learning for nodes only instead of jointly propagating and updating the embeddings of both nodes and edges for target-driven objectives. This paper addresses these limitations by proposing a novel framework, namely the Knowledge Embedding based Graph Convolutional Network (KE-GCN), which combines the power of GCNs in graph-based belief propagation and the strengths of advanced knowledge embedding (a.k.a. knowledge graph embedding) methods, and goes beyond. Our theoretical analysis shows that KE-GCN offers an elegant unification of several well-known GCN methods as specific cases, with a new perspective of graph convolution. Experimental results on benchmark datasets show the advantageous performance of KE-GCN over strong baseline methods in the tasks of knowledge graph alignment and entity classification.